A framework for energy efficient NFV in 5G networks

Al-Quzweeni, Ahmed; Lawey, Ahmed Q.; El-Gorashi, Taisir E. H.; Elmirghani, Jaafar M. H.

doi:10.1109/icton.2016.7550698

Cited by 11 publications

(7 citation statements)

References 11 publications

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“…Figure 11 shows normalized SE and EE responses by varying only the horizontal densification of SBSs (i.e., L) for ζ = 6 and exploiting power, frequency, time and space-domain to reuse spectra to in-building SBSs. Using (20), the minimum optimal value of L can be found in Figure 11, which is approximately equal to 32. This minimum optimal value of L can be justified by the fact that any value of L, which is less than 32, results in decreasing both SE and EE (i.e., consuming more energy per bit transmission to obtain less aggregate capacity per unit frequency).…”

Section: Spatial Exploitationmentioning

confidence: 98%

“…However, recently, environmental protection and energy-saving have also become potential issues raised by global communities, resulting in asking researchers for shifting focus towards energy-efficient network design approaches [13]. Consequently, numerous research studies, e.g., [13][14][15][16][17][18][19][20], have already addressed the issue of EE in mobile networks. Hence, due to increasing the energy consumption from large-scale deployment of small cells [21], in addition to SE, EE has also been considered as one of the key performance metrics for the design of future fifth-generation (5G) and beyond mobile systems [22][23][24].…”

Section: Related Work and Problem Statementmentioning

confidence: 99%

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On Maximizing Energy and Spectral Efficiencies Using Small Cells in 5G and Beyond Networks

Saha

2020

Sensors

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Addressing high capacity at low power as a key design goal envisages achieving high spectral efficiency (SE) and energy efficiency (EE) for the next-generation mobile networks. Because most data are generated in indoor environments, an ultra-dense deployment of small cells (SCs), particularly within multistory buildings in urban areas, is revealed as an effective technique to improve SE and EE by numerous studies. In this paper, we present a framework exploiting the four most interconnected-domain, including, power, time, frequency, and space, in the perspectives of SE and EE. Unlike existing literature, the framework takes advantage of higher degrees of freedom to maximize SE and EE using in-building SCs for 5G and beyond mobile networks. We derive average capacity, SE, and EE metrics, along with defining the condition for optimality of SE and EE and developing an algorithm for the framework. An extensive system-level evaluation is performed to show the impact of each domain on SE and EE. It is shown that employing multiband enabled SC base stations (SBSs) to increase operating spectrum in frequency-domain, reusing spectrum to SBSs more than once per building in spatial-domain, switching on and off each in-building SBS based on traffic availability to reduce SBS power consumption in power-domain, and using eICIC to avoid co-channel interference due to sharing spectrum with SBSs in time-domain can achieve massive SE and EE. Finally, we show that the proposed framework can satisfy SE, EE, as well as user experience data rate requirements for 5G and beyond mobile networks.

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Section: Spatial Exploitationmentioning

confidence: 98%

Section: Related Work and Problem Statementmentioning

confidence: 99%

On Maximizing Energy and Spectral Efficiencies Using Small Cells in 5G and Beyond Networks

Saha

2020

Sensors

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“…Apart from the EE gains, a higher performance would also be achieved because of a reduced distance between the node requesting and the node serving the request. Research in [51] extends the same idea where the EE gains are deemed to be higher with an increased number of virtual function deployments in the access network which typically consumes more energy, about 70% of the entire demand of the end to end network. The suggested topology entails gigabit optical connectivity as the fronthaul technology instead of the Common Public Radio Interface (CPRI) connection between radio and baseband units.…”

Section: Review Of Ee Techniques At the Network Levelmentioning

confidence: 99%

A Survey on Recent Trends and Open Issues in Energy Efficiency of 5G

Usama

Erol‐Kantarci

2019

Sensors

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The rapidly increasing interest from various verticals for the upcoming 5th generation (5G) networks expect the network to support higher data rates and have an improved quality of service. This demand has been met so far by employing sophisticated transmission techniques including massive Multiple Input Multiple Output (MIMO), millimeter wave (mmWave) bands as well as bringing the computational power closer to the users via advanced baseband processing units at the base stations. Future evolution of the networks has also been assumed to open many new business horizons for the operators and the need of not only a resource efficient but also an energy efficient ecosystem has greatly been felt. The deployment of small cells has been envisioned as a promising answer for handling the massive heterogeneous traffic, but the adverse economic and environmental impacts cannot be neglected. Given that 10% of the world’s energy consumption is due to the Information and Communications Technology (ICT) industry, energy-efficiency has thus become one of the key performance indicators (KPI). Various avenues of optimization, game theory and machine learning have been investigated for enhancing power allocation for downlink and uplink channels, as well as other energy consumption/saving approaches. This paper surveys the recent works that address energy efficiency of the radio access as well as the core of wireless networks, and outlines related challenges and open issues.

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“…Mobile network function virtualization (NFV) can be applied over the core and the radio access network (RAN) [9]. This means that we can virtualize these modules and provide on-demand network functions in both of them.…”

Section: Introductionmentioning

confidence: 99%

Base Station Power Optimization for Green Networks Using Reinforcement Learning

Aktaş

Alemdar

2021

Sakarya University Journal of Computer and Information Sciences

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The next generation mobile networks have to provide high data rates, extremely low latency, and support high connection density. To meet these requirements, the number of base stations will have to increase and this increase will lead to an energy consumption issue. Therefore "green" approaches to the network operation will gain importance. Reducing the energy consumption of base stations is essential for going green and also it helps service providers to reduce operational expenses. However, achieving energy savings without degrading the quality of service is a huge challenge. In order to address this issue, we propose a machine learning based intelligent solution that also incorporates a network simulator. We develop a reinforcement-based learning model by using deep deterministic policy gradient algorithm. Our model update frequently the policy of network switches in a way that, packet be forwarded to base stations with an optimized power level. The policies taken by the network controller are evaluated with a network simulator to ensure the energy consumption reduction and quality of service balance. The reinforcement learning model allows us to constantly learn and adapt to the changing situations in the dynamic network environment, hence having a more robust and realistic intelligent network management policy set. Our results demonstrate that energy efficiency can be enhanced by 32% and 67% in dense and sparse scenarios, respectively.

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A framework for energy efficient NFV in 5G networks

Cited by 11 publications

References 11 publications

On Maximizing Energy and Spectral Efficiencies Using Small Cells in 5G and Beyond Networks

On Maximizing Energy and Spectral Efficiencies Using Small Cells in 5G and Beyond Networks

A Survey on Recent Trends and Open Issues in Energy Efficiency of 5G

Base Station Power Optimization for Green Networks Using Reinforcement Learning

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